CN109782126A - Power distribution network incipient fault detection method based on class people's concept learning - Google Patents
Power distribution network incipient fault detection method based on class people's concept learning Download PDFInfo
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Abstract
The invention discloses a kind of power distribution network incipient fault detection methods based on class people's concept learning.Decomposing waveform using wavelet transformation is approximate part and detail section, and wherein approximate part is known as general shape primitive, and detail section is known as the primitive that distorts；Distortion primitive is split as harmonic wave, pulse and other three primitives of distortion according to extreme point；Extract the time relationship between the feature and primitive of primitive；According to the time relationship between the feature and primitive of primitive, the probability distribution of waveform is constructed；According to the probability distribution of variety classes waveform, the judging result of waveform is obtained.The present invention is broken down into general shape and various distortion using voltage, current waveform as one kind of visual concept, by calculating the probability distribution of each ingredient, the probability distribution of waveform entirety can be obtained, to judge waveform catalog.This method is significantly better than traditional detection in demand data amount and accuracy.It is of great significance to the detection processing of power distribution network initial failure.
Description
Technical field
The present invention relates to power distribution network incipient fault detection technical field, specifically a kind of distribution based on class people's concept learning
Net incipient fault detection method.
Background technique
Power supply reliability is the most important evaluation index of power distribution network.Since Distribution Network Equipment is more, region is wide, maintenance work
In terms of processing after being concentrated mainly on failure, such as fault location, Fault Isolation, fault recovery.But with country and sale of electricity
The raising that market requires power supply reliability, the troubleshooting work of distribution, which will not only pay close attention to the service restoration after failure, also to be needed to close
The working method of troubleshooting is transformed into " early warning active in advance by the equipment early warning before infusing failure by " repairing inspection afterwards "
It is eliminated before permanent fault generation, the probability for causing power outage to occur because of equipment fault is greatly reduced by reason ".
Before equipment fault, it often will appear some abnormal Precursory signals, these signals are referred to as initial failure.
A kind of mode that incipient fault detection is detected as power state, new thinking is provided for distribution O&M, so that defect equipment
It can be replaced in advance, improve power supply reliability.Maintenance work amoun is reduced simultaneously, has saved cost.
Initial failure often shows as that the duration is short, occurs repeatedly.This kind of self-reparability failure is right often with electric arc
Insulation and conductor damage.Further, insulation is impaired can bring more failures.So often this kind of failure can be sent out repeatedly
Life is until developing into permanent fault.The reason of causing initial failure is related with device category.In the cable, insulation ag(e)ing is early
The main reason for phase failure.In overhead line, various non-electricity factors are such as blown, animal touching line, branch touching line often draw
Play initial failure.In other power equipments, insulation defect and poor contact can also cause initial failure.
Incipient fault detection is broadly divided into model-driven and two kinds of data-driven, and model-driven is often confined to single mould
Type can not adapt to complicated actual conditions, therefore often use the method for data-driven.Traditional data driving method needs a large amount of
Data and often just for individual scenes, it is therefore desirable to improve.The process pair of class people concept learning analogy mankind observation waveform
Waveform is decomposed, and according to the generating process of the result reconfiguration waveform of decomposition, the identification of waveform is realized by learning this process.
But how by class people's concept learning be applied to power distribution network Incipient Fault Diagnosis in or this field urgent problem to be solved.
Currently without the explanation or report for finding technology similar to the present invention, it is also not yet collected into money similar both at home and abroad
Material.
Summary of the invention
Aiming at the above shortcomings existing in the prior art, the object of the present invention is to by the correlation theory of class people's concept learning
It is introduced into power distribution network Incipient Fault Diagnosis, is proposed by theory analysis a kind of for power distribution network incipient fault detection with method
Power distribution network incipient fault detection method based on class people's concept learning, and the reasonability of verification method.The simulation of class people's concept learning
The mankind identify the process of waveform, and waveform is decomposed into the superposition of heterogeneity, passes through the time relationship between learning component and ingredient
Carry out waveform recognition.Power distribution network incipient fault detection method proposed by the present invention based on class people's concept learning, compared to tradition
Algorithm has the characteristics that can to introduce that priori knowledge, required sample is few, accuracy rate is high.
The present invention is achieved by the following technical solutions.
A kind of power distribution network incipient fault detection method based on class people's concept learning, includes the following steps:
S1: decomposing waveform using wavelet transformation is approximate part and detail section, and wherein approximate part is known as general shape
Primitive, detail section are known as the primitive that distorts；
S2: distortion primitive is split as by harmonic wave, pulse and other three primitives of distortion according to extreme point；
S3: the time relationship between the feature and primitive of primitive is extracted；
S4: according to the time relationship between the feature and primitive of primitive, the probability distribution of waveform is constructed；
S5: according to the probability distribution of variety classes waveform, the judging result of waveform is obtained.
Preferably, in the S1, wavelet transform function is chosen for 5 layers of Meyer function, and general shape takes a5Coefficient, distortion
Take original waveform and a5The difference of coefficient.
Preferably, in the S2, distortion is decomposed, is split as curve according to the extreme point in distortion curve multiple
Each segmentation is combined by segmentation with adjacent sectional, constitutes pulse, harmonic wave and other three primitives of distortion.
Preferably, the rule each segmentation being combined with adjacent sectional are as follows:
If adjacent sectional monotonicity on the contrary, amplitude with time span difference is within 0.8 times to 1.2 times and there are three
Section or more, then constitute harmonic wave；
If adjacent two sections of piecewise monotonics are on the contrary, amplitude is more than threshold value, time span is less than threshold value, then constitutes pulse；
The segmentation that harmonic wave and pulse can not be constituted then is other distortion.
Preferably, amplitude thresholds are set as 0.5 times of fundamental voltage amplitude；Time span threshold value is set as 0.25 times of fundamental wave cycle.
Preferably, in the S3, the feature extraction of primitive, including principle is extracted as follows:
For general shape zo, extract the amplitude A of each cycleo, time span ToAnd DC component Aoft；For harmonic wave
zh, extract amplitude Ah, frequency fhAnd total time length th；For pulse zp, extract amplitude Ap, pulsewidth tp；For other distortion
zother, do not extract feature.
Preferably, in the S3, the time relationship between primitive include general shape and distortion between time relationship and
Time relationship between distortion and distortion；Wherein:
Time relationship between the general shape and distortion is known as opposite fundamental wave position Po, opposite fundamental wave position PoDescription
Position of the distortion initial time in general shape, this position is indicated using phase angle；
Distort distortion between time relationship include:
Interval time tint, the time interval between two adjacent distortion is described；
Single-phase primitive is to PPuni, describe initial time in same phase voltage or current waveform identical or close two it is abnormal
Become；
Three-phase primitive is to PPtriThree identical or close distortion of initial time in three-phase voltage or current waveform are described.
Preferably, the time relationship between the time relationship and distortion and distortion between the general shape and distortion
In, ignore time relationship relevant to other distortion.
Preferably, in the S4, the waveform probability distribution formula of construction are as follows:
In formula,For unknown waveforms example, ψwFor known waveform type, noise Normal Distribution SN~N (μ, σ2), base
First number is κ, and primitive type is z={ zo,zh,zp,zother, primitive feature parameter is p, and the time relationship between primitive is R；
According to the three-phase current recorded in distribution anomalous event, summation obtains neutral point current, and generation 7 is different types of
Waveform (IA,IB,IC,IN,UA,UB,UC), obtain the probability distribution formula in variety classes waveform of anomalous event are as follows:
In formula,For unknown event example, ψEFor known event type, waveform w={ IA,IB,IC,IN,UA,UB,UC}。
Preferably, in the S5, according to probability distribution of the anomalous event in variety classes waveform, judge anomalous event
Type comparesSize in variety classes waveform is maximized corresponding waveform catalog, obtains sentencing for waveform
Disconnected result.
Compared with prior art, the invention has the following beneficial effects:
A kind of power distribution network incipient fault detection method based on class people's concept learning provided by the present invention, by class people's concept
The correlation theory and method of study are introduced into power distribution network incipient fault detection, are proposed by theory analysis for power distribution network early stage
The detection algorithm of failure, and the reasonability of verification algorithm.The one kind of voltage, current waveform as visual concept is broken down into big
Shape and various distortion are caused, by calculating the probability distribution of each ingredient, the probability distribution of waveform entirety can be obtained, to sentence
Disconnected waveform catalog.Power distribution network incipient fault detection method provided by the present invention based on class people's concept learning, in demand data
It is significantly better than traditional detection in amount and accuracy, is of great significance to the detection processing of power distribution network initial failure.
Detailed description of the invention
Upon reading the detailed description of non-limiting embodiments with reference to the following drawings, other feature of the invention,
Objects and advantages will become more apparent upon:
Fig. 1 is waveform decomposition diagram provided by one embodiment of the invention；
Fig. 2 is Distortion Decomposition schematic diagram provided by one embodiment of the invention；
Fig. 3 is that the time relationship between primitive provided by one embodiment of the invention defines schematic diagram；Wherein, (a) is substantially
Time relationship between shape and distortion defines schematic diagram；(b) time relationship between distortion and distortion defines schematic diagram；
Fig. 4 is waveform generating process schematic diagram provided by one embodiment of the invention；
Fig. 5 is the power distribution network incipient fault detection method work based on class people's concept learning provided by one embodiment of the invention
Make flow chart.
Specific embodiment
Elaborate below to the embodiment of the present invention: the present embodiment carries out under the premise of the technical scheme of the present invention
Implement, the detailed implementation method and specific operation process are given.It should be pointed out that those skilled in the art
For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to guarantor of the invention
Protect range.
Embodiment
As shown in figure 5, a kind of power distribution network incipient fault detection method based on class people's concept learning is present embodiments provided,
Include the following steps:
Step S1: decomposing waveform using wavelet transformation is approximate part and detail section, wherein by approximate part and thin
This two parts of section part are referred to as general shape primitive and distortion primitive.
Step S2: distortion (detail section) is split as by harmonic wave, pulse and other three kinds of primitives of distortion according to extreme point.
Step S3: the time relationship between the feature and primitive of primitive is extracted.
Step S4: according to the probability distribution of the time relationship construction waveform between the feature and primitive of primitive.
Step S5: the judging result of waveform is obtained according to the probability distribution of variety classes waveform.
With reference to the accompanying drawing, the technical solution of the above embodiment of the present invention is described in further detail.
As shown in Figure 1, to carry out the waveform decomposition diagram of wavelet decomposition to original waveform, wavelet function choice is 5 layers
Meyer function, general shape take a5Coefficient, distortion take original waveform and a5The difference of coefficient obtains decomposition result shown in Fig. 1.For
Distortion (detail section) in Fig. 1 continues to decompose, and curve is split as segment according to the extreme point in distortion curve, such as schemes
Each section is combined by the first step shown in 2 with surrounding segment, constitutes pulse, harmonic wave, other three kinds of primitives of distortion, such as Fig. 2
Shown in second step.Rule of combination is as follows: if 1) adjacent segment monotonicity is opposite, amplitude and time span difference it is smaller and
There are three sections or more, then constitute harmonic wave (1,2,3,4 section of combination as shown in Figure 2)；2) if two sections of adjacent monotonicities are opposite,
Amplitude is less than threshold value more than threshold value (being set as 0.5 times of fundamental voltage amplitude here), time span and (is set as 0.25 times of fundamental wave week here
Wave), then constitute pulse (5,6 sections of combinations as shown in Figure 2)；3) segment that can not constitute harmonic wave and pulse is known as other distortion
(as shown in Figure 27,8 sections).Here general shape, harmonic wave, pulse, other distortion are referred to as primitive.
Primitive feature extracts as follows: for general shape zo, extract the amplitude A of each cycleo, time span ToAnd it is straight
Flow component Aoft；For harmonic wave zh, extract amplitude Ah, frequency fh, total time length th；For pulse zp, extract amplitude Ap, pulsewidth
tp；For other z that distortother, due to zotherIt is little with initial failure relationship, so not extracting its feature.It is equally deposited between primitive
In time relationship, primitive can be divided into two kinds: general shape and distortion (harmonic wave, pulse, other distortion), the time relationship between primitive
The time relationship that can also be divided between the time relationship and distortion and distortion between general shape and distortion.Similarly, it does not beg for
By with zotherRelevant time relationship.Time relationship between general shape and distortion primitive is known as opposite fundamental wave position Po, Po
Position of the distortion primitive initial time in general shape is described, this position is indicated with phase angle.Distort primitive between when
Between relationship have it is following several: interval time tint, single-phase primitive is to PPuniWith three-phase primitive to PPtri。tintTwo phases are described
Time interval between neighbour's distortion primitive.Single-phase primitive is to PPuniSame phase voltage is described, initial time phase in current waveform
With/and its two close distortion primitives.Three-phase primitive is to PPtriInitial time phase in three-phase voltage/current waveform is described
With/and its three close distortion primitives.PPuniAnd PPtriA kind of correlativity is described, uses n hereuni,ntriTable respectively
Show the quantity of the two.The schematic diagram of the above time relationship such as Fig. 3 (a) and (b) are shown.
So far, a waveform is uniquely decomposed into the combination of the above primitive, and primitive feature, time relationship are used for
This combination is described.Next, proposing a generating process of waveform based on above-mentioned decomposition, as shown in Figure 4.This process description
It is as follows: to select some primitives (primitive type is 4, and every kind of primitive quantity is any) in primitive library first, each primitive has respectively
Characteristic parameter；These primitives are sequentially combined into waveform, these sequencings constitute the time relationship between primitive.
Belong to kind of a class hierarchy above.Waveform catalog is determined by failure cause, faulty equipment, trouble location.In actual conditions, with type wave
Shape is influenced by line parameter circuit value, grid structure, load condition, sensor parameters, noise etc., can show as different a examples.Performance
It is in primitive level are as follows: certain variation can occur for the characteristic parameter and time relationship of primitive, while can introduce noise.More than
Belong to an example level.
According to the above generating process, the probability distribution formula of waveform can be derived:
In formulaFor unknown waveforms example, ψwFor known waveform type, noise Normal Distribution SN~N (μ, σ2), primitive
Number is κ, and primitive type is z={ zo,zh,zp,zother, primitive feature parameter is p, and time relationship is R between primitive.
For the anomalous event together in distribution, often record has its corresponding three-phase voltage, three-phase current, by three
The available neutral point current of phase current summation, this creates the terminal 7 different types of waveform (IA,IB,IC,IN,UA,UB,
UC).Therefore probability distribution of the anomalous event in variety classes waveform is writeable together are as follows:
In formula,For unknown event example, ψEFor known event type, waveform w={ IA,IB,IC,IN,UA,UB,UC}。
The type that may determine that anomalous event according to probability distribution of the anomalous event in variety classes waveform, that is, compareSize in variety classes waveform is maximized corresponding waveform catalog.
100 known samples are taken to be trained, event is respectively single-phase single-revolution wave initial failure, single-phase more cycles morning
Phase failure, phase fault initial failure, transient interference and permanent fault (respectively marked as 1,2,3,4,5).With other 200
A unknown sample is tested, and experimental result is as shown in table 1.It can be seen that being based on class people provided by the above embodiment of the present invention
The power distribution network incipient fault detection method of concept learning, accuracy rate is high, and required data volume is less.
Table 1
Specific embodiments of the present invention are described above.It is to be appreciated that the invention is not limited to above-mentioned
Particular implementation, those skilled in the art can make various deformations or amendments within the scope of the claims, this not shadow
Ring substantive content of the invention.
Claims (10)
1. a kind of power distribution network incipient fault detection method based on class people's concept learning, which comprises the steps of:
S1: decomposing waveform using wavelet transformation is approximate part and detail section, and wherein approximate part is known as general shape primitive,
Detail section is known as the primitive that distorts；
S2: distortion primitive is split as by harmonic wave, pulse and other three primitives of distortion according to extreme point；
S3: the time relationship between the feature and primitive of primitive is extracted；
S4: according to the time relationship between the feature and primitive of primitive, the probability distribution of waveform is constructed；
S5: according to the probability distribution of variety classes waveform, the judging result of waveform is obtained.
2. the power distribution network incipient fault detection method according to claim 1 based on class people's concept learning, which is characterized in that
In the S1, wavelet transform function is chosen for 5 layers of Meyer function, and general shape takes a5Coefficient, distortion take original waveform and a5System
Several differences.
3. the power distribution network incipient fault detection method according to claim 1 based on class people's concept learning, which is characterized in that
In the S2, distortion is decomposed, curve is split as by multiple segmentations according to the extreme point in distortion curve, by each segmentation
It is combined with adjacent sectional, constitutes pulse, harmonic wave and other three primitives of distortion.
4. the power distribution network incipient fault detection method according to claim 3 based on class people's concept learning, which is characterized in that
The rule that each segmentation is combined with adjacent sectional are as follows:
If adjacent sectional monotonicity on the contrary, amplitude and time span difference within 0.8 times to 1.2 times and there are three sections with
On, then constitute harmonic wave；
If adjacent two sections of piecewise monotonics are on the contrary, amplitude is more than threshold value, time span is less than threshold value, then constitutes pulse；
The segmentation that harmonic wave and pulse can not be constituted then is other distortion.
5. the power distribution network incipient fault detection method according to claim 4 based on class people's concept learning, which is characterized in that
Amplitude thresholds are set as 0.5 times of fundamental voltage amplitude；Time span threshold value is set as 0.25 times of fundamental wave cycle.
6. the power distribution network incipient fault detection method according to claim 1 based on class people's concept learning, which is characterized in that
In the S3, the feature extraction of primitive, including principle is extracted as follows:
For general shape zo, extract the amplitude A of each cycleo, time span ToAnd DC component Aoft；For harmonic wave zh, mention
Take amplitude Ah, frequency fhAnd total time length th；For pulse zp, extract amplitude Ap, pulsewidth tp；For other z that distortother,
Do not extract feature.
7. the power distribution network incipient fault detection method according to claim 1 based on class people's concept learning, which is characterized in that
In the S3, the time relationship between primitive includes between time relationship and distortion and distortion between general shape and distortion
Time relationship；Wherein:
Time relationship between the general shape and distortion is known as opposite fundamental wave position Po, opposite fundamental wave position PoDescription distortion
Position of the initial time in general shape, this position are indicated using phase angle；
Distort distortion between time relationship include:
Interval time tint, the time interval between two adjacent distortion is described；
Single-phase primitive is to PPuni, two identical or close distortion of initial time in same phase voltage or current waveform are described；
Three-phase primitive is to PPtriThree identical or close distortion of initial time in three-phase voltage or current waveform are described.
8. the power distribution network incipient fault detection method according to claim 7 based on class people's concept learning, which is characterized in that
In the time relationship between time relationship and distortion and distortion between the general shape and distortion, ignore and other distortion
Relevant time relationship.
9. the power distribution network incipient fault detection method according to claim 1 based on class people's concept learning, which is characterized in that
In the S4, the waveform probability distribution formula of construction are as follows:
In formula,For unknown waveforms example, ψwFor known waveform type, noise Normal Distribution SN~N (μ, σ2), primitive number
For κ, primitive type is z={ zo,zh,zp,zother, primitive feature parameter is p, and the time relationship between primitive is R；
According to the three-phase current recorded in distribution anomalous event, summation obtains neutral point current, generates 7 different types of waveforms
(IA,IB,IC,IN,UA,UB,UC), obtain the probability distribution formula in variety classes waveform of anomalous event are as follows:
In formula,For unknown event example, ψEFor known event type, waveform w={ IA,IB,IC,IN,UA,UB,UC}。
10. the power distribution network incipient fault detection method according to claim 9 based on class people's concept learning, feature exist
According to probability distribution of the anomalous event in variety classes waveform, judging the type of anomalous event, that is, compare in the S5Size in variety classes waveform is maximized corresponding waveform catalog, obtains the judging result of waveform.
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| CN201811607507.8A CN109782126B (en) | 2018-12-27 | 2018-12-27 | Power distribution network early fault detection method based on humanoid concept learning |
| JP2019538613A JP7107580B2 (en) | 2018-12-27 | 2019-03-19 | Methods for detecting premature failures in distribution networks |
| GB1912002.1A GB2582676B (en) | 2018-12-27 | 2019-03-19 | Early Failure Detection Method for Power Distribution Network Equipment Based On Human-Level Concept Learning |
| PCT/CN2019/078575 WO2020133735A1 (en) | 2018-12-27 | 2019-03-19 | Method for early fault detection of power distribution network based on humanoid concept learning |
| US16/674,233 US11143686B2 (en) | 2018-12-27 | 2019-11-05 | Method for detecting power distribution network early failure |
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| CN113884803A (en) * | 2021-08-27 | 2022-01-04 | 国网山东省电力公司日照供电公司 | Distribution network fault studying and judging method |
| CN113985733A (en) * | 2021-10-26 | 2022-01-28 | 云南电网有限责任公司电力科学研究院 | Power distribution network fault identification method based on adaptive probability learning |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113884803A (en) * | 2021-08-27 | 2022-01-04 | 国网山东省电力公司日照供电公司 | Distribution network fault studying and judging method |
| CN113985733A (en) * | 2021-10-26 | 2022-01-28 | 云南电网有限责任公司电力科学研究院 | Power distribution network fault identification method based on adaptive probability learning |
| CN113985733B (en) * | 2021-10-26 | 2023-11-17 | 云南电网有限责任公司电力科学研究院 | Power distribution network fault identification method based on self-adaptive probability learning |
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| WO2020133735A1 (en) | 2020-07-02 |
| JP7107580B2 (en) | 2022-07-27 |
| CN109782126B (en) | 2021-01-01 |
| JP2021512377A (en) | 2021-05-13 |
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